1. Field of the Invention
This invention generally relates to methods and systems for determining a process window for a process performed on a specimen with adaptive sampling.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Process window qualification (PWQ) is a type of inspection performed on a specimen fabricated in a particular way that is essential to check if a specific chip design can be manufactured (free of critical hot spots) and to decide about the optimal parameters for a lithography process (e.g., focus/exposure). Usually, a focus-exposure modulated specimen is printed to simulate different process window conditions. The specimen is then inspected using a relatively sensitive bright field (BF) inspection tool. The detected defects are divided into bins by a design-based algorithm that classifies the defects by type of printing error (a unique design structure is associated with each bin). To determine how a printing error is affecting the chip yield at different process modulations, a defect sampling strategy followed by scanning electron microscope (SEM) review is performed. For example, a few representative defects from each bin can be visited at different die modulations. This time consuming procedure checks how a structure responds to changes in lithography parameters (focus/exposure) and finally the process window limits are determined. To increase sensitivity, a second iteration is sometimes performed. In that case, the previously identified printing errors can be used as care areas in the wafer inspection. The complete procedure may then be repeated.
The current best known method (BKM) for process window determination may also leverage existing software and algorithm capabilities in design-based care area technology for analyzing PWQ wafers. This approach has been designed for improving the quality of the PWQ results such as hot spot discovery and process window qualification. Some PWQ inspections introduce a second inspection pass into the flow. The first scan is designed for hot spot discovery, and the second scan is designed for determining the process window by monitoring the hot spots discovered in the first scan.
There are, however, several disadvantages to currently used methods for PWQ. For example, one disadvantage of the currently used methods is related to the sampling strategy for SEM review. The assumption on which currently used sampling is based is that a systematic printing error is accurately represented by a few selected defects/locations observed using SEM review (representative sampling). If the assumption is invalid, hot spots can be missed or process window can be incorrectly reported. In addition, the benefits of the two pass PWQ inspection described above are achieved by introducing design-based care area capabilities, two pass PWQ inspection, increased sample size, and combining several available sampling strategies into a more efficient overall system. However, no attempt has been made so far to develop new sampling strategies that are aligned with the two distinct goals of hot spot discovery and process window identification. In particular, the sampling method currently employed in the second pass PWQ inspection may be composed of a variety of sampling schemes such as PWQ sampling, design-based binning (DBB) sampling, diversity sampling, and care area group code (CAGCode) sampling, all of which have some desirable features for process window determination, but none of them is actually very well suited for it. All of the sampling methods, when used in combination, actually can waste a lot of the sample size due to overlapping criteria and goals. There is simply no sampling method specifically targeted for process window determination.
Accordingly, it would be advantageous to develop systems and/or methods for determining a process window for a process performed on a specimen with adaptive sampling that do not have one or more of the disadvantages described above.